Method of analyzing and processing signals

ABSTRACT

A physiological measurement system is disclosed which can take a pulse oximetry signal such as a photoplethysmogram from a patient and then analyse the signal to measure physiological parameters including respiration, pulse, oxygen saturation and movement. The system comprises a pulse oximeter which includes a light emitting device and a photodetector attachable to a subject to obtain a pulse oximetry signal; analogue to digital converter means arranged to convert said pulse oximetry signal into a digital pulse oximetry signal; signal processing means suitable to receive said digital pulse oximetry signal and arranged to decompose that signal by wavelet transform means; feature extraction means arranged to derive physiological information from the decomposed signal; an analyser component arranged to collect information from the feature extraction means; and data output means arranged in communication with the analyser component.

1. INTRODUCTIONS: PROBLEM DOMAIN/FIELD OF INVENTION

The present invention relates to a method of analysing and processingsignals. More specifically the invention relates to the analysis andprocessing of photoplethysmogram (PPG) signals. The invention useswavelet transform methods to derive clinically useful information fromthe PPG including information regarding the respiration, pulse, oxygensaturation, and patient movement. This information may be used within adevice to monitor the patient within a range of environments includingthe hospital and home environments. In one preferred embodiment thedevice may be used to detect irregularities in one or more of thederived signals: respiration, pulse, oxygen saturation and movement. Thedevice allows output of this information in a clinically useful form andincorporates an alarm which is triggered when one or a combination ofsignal irregularities are detected.

Of particular note is that the utility of current pulse oximeter devicesis greatly increased through the provision of a robust measure ofpatient respiration directly from the PPG signal.

2. BACKGROUND 2.1 Blood Oxygen Saturation and its Measurement

Oximetry is an optical method for measuring oxygen saturation in blood.Oximetry is based on the ability of different forms of haemoglobin toabsorb light of different wavelengths. Oxygenated haemoglobin (HbO₂)absorbs light in the red spectrum and deoxygenated or reducedhaemoglobin (RHb) absorbs light in the near-infrared spectrum. When redand infrared light is passed through a blood vessel the transmission ofeach wavelength is inversely proportional to the concentration of HbO₂and RHb in the blood. Pulse oximeters can differentiate the alternatinglight input from arterial pulsing from the constant level contributionof the veins and other non-pulsatile elements. Only the alternatinglight input is selected for analysis. Pulse oximetry has been shown tobe a highly accurate technique. Modern pulse oximeter devices aim tomeasure the actual oxygen saturation of the blood (SaO₂) byinterrogating the red and infrared PPG signals. This measurement isdenoted SpO₂. The aim of modern device manufacturers is to achieve thebest correlation between the pulse oximeter measurement given by thedevice and the actual blood oxygen saturation of the patient. It isknown to those skilled in the art that in current devices a ratioderived from the photoplethysmogram (PPG) signals acquired at thepatients body is used to determine the oxygen saturation measurementusing a look up table containing a pluracy of corresponding ratio andsaturation values. Modern pulse oximeter devices also measure patientheart rate. Current devices do not provide a measure of respirationdirectly from the PPG signal. Additional expensive and obtrusiveequipment is necessary to obtain this measurement.

2.2 Time-Frequency Analysis in Wavelet Space

The wavelet transform of a signal x(t) is defined as

$\begin{matrix}{{T\left( {a,b} \right)} = {\frac{1}{\sqrt{a}}{\int_{- \infty}^{+ \infty}{{x(t)}{\psi^{*}\left( \frac{t - b}{a} \right)}\ {t}}}}} & \lbrack 1\rbrack\end{matrix}$

where ψ*(t) is the complex conjugate of the wavelet function ψ(t) a isthe dilation parameter of the wavelet and b is the location parameter ofthe wavelet. The transform given by equation (1) can be used toconstruct a representation of a signal on a transform surface. Thetransform may be regarded as a time-scale representation or atime-frequency representation where the characteristic frequencyassociated with the wavelet is inversely proportional to the scale a. Inthe following discussion ‘time-scale’ and ‘time-frequency’ may beinterchanged. The underlying mathematical detail required for theimplementation within a time-scale or time-frequency framework can befound in the general literature, e.g. the text by Addison (2002).

The energy density function of the wavelet transform, the scalogram isdefined as

S(a,b)=|T(a,b)|²  [2]

where ‘| |’ is the modulus operator. The scalogram may be rescaled foruseful purpose. One common rescaling is defined as

$\begin{matrix}{{S_{R}\left( {a,b} \right)} = \frac{{{T\left( {a,b} \right)}}^{2}}{a}} & \lbrack 3\rbrack\end{matrix}$

and is useful for defining ridges in wavelet space when, for example,the Morlet wavelet is used. Ridges are defined as the locus of points oflocal maxima in the plane. Any reasonable definition of a ridge may beemployed in the method. We also include as a definition of a ridgeherein paths displaced from the locus of the local maxima. A ridgeassociated with only the locus of points of local maxima in the plane welabel a ‘maxima ridge’. For practical implementation requiring fastnumerical computation the wavelet transform may be expressed in Fourierspace and the Fast Fourier Transform (FFT) algorithm employed. Howeverfor a real time application the temporal domain convolution expressed byequation (1) may be more appropriate. In the discussion of thetechnology which follows herein the ‘scalogram’ may be taken to theinclude all reasonable forms of rescaling including but not limited tothe original unscaled wavelet representation, linear rescaling and anypower of the modulus of the wavelet transform may be used in thedefinition.

As described above the time-scale representation of equation (1) may beconverted to a time-frequency representation. To achieve this, we mustconvert from the wavelet a scale (which can be interpreted as arepresentative temporal period) to a characteristic frequency of thewavelet function. The characteristic frequency associated with a waveletof arbitrary a scale is given by

$\begin{matrix}{f = \frac{f_{c}}{a}} & \lbrack 4\rbrack\end{matrix}$

where f_(c), the characteristic frequency of the mother wavelet (i.e. ata=1), becomes a scaling constant and f is the representative orcharacteristic frequency for the wavelet at arbitrary scale a.

Any suitable wavelet function may be used in the method describedherein. One of the most commonly used complex wavelets, the Morletwavelet, is defined as:

ψ(t)=π^(−1/4)(e ^(f2nf) ⁰ ^(t) −e ^(−(2nf) ⁰ ⁾ ³ ^(/2))e ^(−t) ³^(/2)  [5]

where f₀ is the central frequency of the mother wavelet. The second termin the brackets is known as the correction term, as it corrects for thenon-zero mean of the complex sinusoid within the Gaussian window. Inpractice it becomes negligible for values of f₀>>0 and can be ignored,in which case, the Morlet wavelet can be written in a simpler form as

$\begin{matrix}{{\psi (t)} = {\frac{1}{\pi^{1/4}}^{{2\pi}\; f_{0}t}^{{- t^{2}}/2}}} & \lbrack 6\rbrack\end{matrix}$

This wavelet is simply a complex wave within a Gaussian envelope. Weinclude both definitions of the Morlet wavelet in our discussion here.However, note that the function of equation (6) is not strictly awavelet as it has a non-zero mean, i.e. the zero frequency term of itscorresponding energy spectrum is non-zero and hence it is inadmissible.However, it will be recognised by those skilled in the art that it canbe used in practice with f₀>>0 with minimal error and we include it andother similar near wavelet functions in our definition of a waveletherein. A more detailed overview of the underlying wavelet theory,including the definition of a wavelet function, can be found in thegeneral literature, e.g. the text by Addison (2002). Herein we show howwavelet transform features may be extracted from the waveletdecomposition of pulse oximeter signals and used to provide a range ofclinically useful information within a medical device.

3. WAVELET FEATURE EXTRACTION

In this section, methods are described for the extraction and use ofwavelet features from the PPG signals for use in the provision ofclinically useful information. These are incorporated within a medicaldevice and the information is output in a range of formats for use inthe monitoring of the patient. The device comprises four key componentsfor the utilization of the wavelet transform information, these are thePulse Component, Respiration Monitoring Component Oxygen SaturationComponent and the Movement Component. The underlying theory pertainingto these components is detailed below.

3.1 Pulse Component

Pertinent repeating features in the signal gives rise to atime-frequency band in wavelet space or a rescaled wavelet space. Forexample the pulse component of a photoplethysmogram (PPG) signalproduces a dominant band in wavelet space at or around the pulsefrequency. FIG. 1( a) and (b) contains two views of a scalogram derivedfrom a PPG signal. The figures show an example of the band caused by thepulse component in such a signal. The pulse band is located between thedashed lines in the plot of FIG. 1( a). The band is formed from a seriesof dominant coalescing features across the scalogram. This can beclearly seen as a raised band across the transform surface in FIG. 1( b)located within a region at just over 1 Hz, i.e. 60 breaths per minute.The maxima of this band with respect to frequency is the ridge. Thelocus of the ridge is shown as a black curve on top of the band in FIG.1( b). By employing a suitable rescaling of the scalogram, such as thatgiven in equation 3, we can relate the ridges found in wavelet space tothe instantaneous frequency of the signal. In this way the pulsefrequency (pulse rate) may be obtained from the PPG signal. Instead ofrescaling the scalogram, a suitable predefined relationship between thefrequency obtained from the ridge on the wavelet surface and the actualpulse frequency may also be used to determine the pulse rate.

By mapping the time-frequency coordinates of the pulse ridge onto thewavelet phase information gained through the wavelet transform,individual pulses may be captured. In this way both times betweenindividual pulses and the timing of components within each pulse can bemonitored and used to detect heart beat anomalies, measure arterialsystem compliance, etc. Alternative definitions of a ridge may beemployed. Alternative relationships between the ridge and the pulsefrequency may be employed.

3.2 Respiration Monitoring Component

The respiration monitoring component uses wavelet based methods for themonitoring of patient respiration. This can include the measurement ofbreathing rate and the identification of abnormal breathing patternsincluding the cessation of breathing. A key part of the respirationmonitoring component is the use of secondary wavelet feature decoupling(SWFD) described below. The information concerning respiration gainedfrom the application of SWFD can then be compared and/or combined withrespiration information from other methods to provide a respirationmeasure output.

As stated above pertinent repeating features in the signal give rise toa time-frequency hand in wavelet space or a rescaled wavelet space. Fora periodic signal this band remains at a constant frequency level in thetime frequency plane. For many real signals, especially biologicalsignals, the band may be non-stationary; varying in characteristicfrequency and/or amplitude over time. FIG. 2 shows a schematic of awavelet transform of a signal containing two pertinent componentsleading to two bands in the transform space. These bands are labeledband A and band B on the three-dimensional (3-D) schematic of thewavelet surface. We define the band ridge as the locus of the peakvalues of these bands with respect to frequency. For the purposes of thediscussion of the method we assume that band B contains the signalinformation of interest. We will call this the ‘primary band’. Inaddition, we assume that the system from which the signal originates,and from which the transform is subsequently derived, exhibits some formof coupling between the signal components in band A and band B.

When noise or other erroneous features are present in the signal withsimilar spectral characteristics of the features of band B then theinformation within band B can become ambiguous, i.e. obscured,fragmented or missing. In this case the ridge of band A can be followedin wavelet space and extracted either as an amplitude signal or afrequency signal which we call the ‘ridge amplitude perturbation (RAP)signal’ and the ‘ridge frequency perturbation (RFP) signal’respectively. The RAP and RFP signals are extracted by projecting theridge onto the time-amplitude or time-frequency planes respectively. Thetop plots of FIG. 3 shows a schematic of the RAP and RFP signalsassociated with ridge A in FIG. 2. Below these RAP and RFP signals wecan see schematics of a further wavelet decomposition of these newlyderived signals. This secondary wavelet decomposition allows forinformation in the spectral region of band B in FIG. 2 to be madeavailable as band C and band D. The ridges of bands C and D can serve asinstantaneous time-frequency characteristic measures of the signalcomponents causing bands C and D. This method, which we call SecondaryWavelet Feature Decoupling (SWFD), therefore allows informationconcerning the nature of the signal components associated with theunderlying physical process causing the primary band B (FIG. 2) to beextracted when band B itself is obscured in the presence of noise orother erroneous signal features.

An example of the SWFD method used on a PPG signal to detect patientbreathing from the ridge associated with patient pulse is shown in FIGS.4 and 5. During the experiment from which the signal was taken thepatient was breathing regularly at breaths of 6 seconds duration (=0.167Hz).

FIG. 4( a) contains the scalogram derived from the PPG trace takenduring the experiment. Two dominant bands appear in the plot: the pulseband and a band associated with patient breathing. These are marked Pand B respectively in the plot. In this example we are concerned withthe detection of breathing through time and hence here the breathingband is the primary band. The pulse band appears at just over 1 Hz, or60 beats per minute: the beat frequency of the heart and the breathingband appears at 0.167 Hz corresponding to the respiration rate. However,the identification of breathing features is often masked by other lowfrequency artefact in these signals. One such low frequency artefactfeature, ‘F’, is indicated in the plot within the dotted ellipse markedon the scalogram where it can be seen to interfere with the breathingband. FIG. 4( b) contains a 3-D view of the scalogram plot shown in FIG.4( a). From the 3-D plot we can see that the low frequency artefactfeature causes a bifurcation of the breathing band at the location shownby the arrow in the plot. The pulse ridge is also shown on FIG. 4( b),indicated by the black curve along the pulse band. This is the locus ofthe maxima with respect to frequency along the pulse band.

FIG. 4( c) contains the RAP signal derived from the pulse ridge shown inFIG. 4( b) where the pulse ridge is followed and its amplitude isplotted against time. The top plot of FIG. 4( c) contains the whole RAPsignal. The lower plot of FIG. 4( c) contains a blow up of the RAPsignal over a 50 seconds interval. An obvious modulation with a periodof 6 seconds can be seen in this blow up. The top plot of FIG. 4( d)contains the whole RFP signal corresponding to the pulse ridge in FIG.4( b). The lower plot of FIG. 4( d) contains a blow up of the RFP signalover 50 seconds. Again an obvious modulation (of 6 second period) can beseen in this blow up.

A second wavelet transform was then performed on the RAP and RFPsignals. The resulting scalograms corresponding to the RAP and RFPsignals are shown in FIGS. 5 a and 5 b respectively and the 3-D plots ofthese scalograms are shown in FIGS. 5 c and 5 d respectively. Thebreathing ridges derived from the RAP and RFP scalograms aresuperimposed on the 3-D scalograms. The RAP scalogram is the cleaner ofthe two and can be seen not to contain interference from the artefactfeature ‘F’ found in the original signal scalogram of FIG. 4( a) Forthis example the RAP scalogram provides the best solution for theremoval of erroneous signal features and the identification of thebreathing band when compared to the original scalogram and the RFPscalogram. In practice all three scalograms are compared and the optimalscalogram or combination of scalograms for the extraction of theinformation required is determined.

Through experimentation covering a variety of patient groups (e.g.adult, child, neonate) we have found that for certain signals the methodcan be enhanced by incorporating paths displaced from the band ridge inthe SWFD method. In these cases the RAP signals derived from thedisplaced path exhibits much larger oscillations (compared to the lowfrequency background waveform) than those of the original ridge path. Wefind that this enhancement allows us to better detect the breathingcomponent within the SWFD method. Hence we extend our definition of asurface ridge as employed in the method to include paths displaced fromthe locus of the peak values contours at a selected level of the pulseband, and in general any reasonably constructed path within the vicinityof the pertinent feature under investigation, where the vicinity istaken to be within the region of the corresponding band.

From the above example it can be seen how a secondary wavelet transformof wavelet transform ridge information derived from the pulse band ridgemay be used to provide a clearer manifestation of the breathing featuresin wavelet space from which pertinent breathing information may bederived.

The SWFD method described above can form the basis of completely newalgorithms for incorporation within devices which require the detectionof otherwise masked signal components. Herein, we show the applicationof the method to the detection of breathing features from within thephotoplethysmogram, although it will be recognised by those skilled inthe art that the method may be applied to other problematic signals.

In practice, both the original direct observation of the primary bandand the indirect observation through perturbations to the secondary bandmay be employed simultaneously and the optimal time-frequencyinformation extracted.

Those skilled in the art will recognise that modifications andimprovements can be incorporated within the methodology outlined hereinwithout departing from the scope of the invention.

Those skilled in the art will recognise that the above methods may beperformed using alternative time-frequency representations of thesignals where the amplitude in the time-frequency transform space can berelated to the amplitude of pertinent features within the signal. Inaddition the decomposition of the original signal and the subsequentdecompositions of the RFP and RAP scalograms may be performed, each witha different time-frequency method. However, in the preferred method thecontinuous wavelet transform is employed in all decompositions, althoughdifferent wavelet functions may be employed in each of the wavelettransforms employed in the method.

The preferred method detailed herein departs from alternate methods toprobe the time-frequency information within wavelet space which followpaths of constant frequency in wavelet space. The current methodinvolves following a selected path in wavelet space from which newsignals are derived. This allows signal components with non-stationaryfrequency characteristics to be followed and analysed to provideinformation of other signal components which may also exhibitnon-stationary behaviour.

It will be obvious to those skilled in the art that the method relies onhigh resolution in wavelet space hence the continuous wavelet transformis the preferred method. (The time-frequency discretisation employed bythe discrete wavelet transform and the stationary wavelet transform is,in general, too coarse for the useful application of the method.) Thecontinuous wavelet transform is implemented in the method through a finediscretisation in both time and frequency.

Although the method herein has been described in the context of thedetection of breathing features from the pulse band of the wavelettransform of the photoplethysmogram, those skilled in the art willrecognise that the method has wide applicability to other signalsincluding, but not limited to: other biosignals (e.g. theelectrocardiogram, electroencephalogram, electrogastrogram,electromyogram, heart rate signals, pathological sounds, and ultrasound)dynamic signals, non-destructive testing signals, condition monitoringsignals, fluid signals, geophysical signals, astronomical signals,electrical signals, financial signals including financial indices soundand speech signals chemical signals, and meteorological signalsincluding climate signals.

In summary a method for the decomposition of signals using wavelettransforms has been described which allows for underlying signalfeatures which are otherwise masked to be detected. The method isdescribed in the following steps

-   -   (a) A wavelet transform decomposition of the signal is made.    -   (b) The transform surface is inspected in the vicinity of the        characteristic frequency of the pertinent signal feature to        detect the dominant band (the primary band) associated with the        pertinent feature. This band is then interrogated to reveal        information corresponding to the pertinent feature. This        interrogation may include ridge following methods for        identification of localised frequencies in the time-frequency        plane.    -   (c) A secondary band is then identified outwith the region of        the pertinent feature and its ridge identified    -   (d) The time-frequency and time-amplitude locus of points on the        secondary ridge are then extracted. These new signals are        denoted the ‘ridge amplitude perturbation (RAP) signal’ and the        ‘ridge frequency perturbation (RFP) signal’ respectively.    -   (e) A wavelet transformation of the RAP and RFP signals is then        carried out to give the RAP and RFP scalograms respectively.    -   (f) These secondary scalograms are then interrogated to reveal        information in the region of the primary band of the original        scalogram. This interrogation may include ridge following        methods for identification of localised frequencies in the        time-frequency plane.    -   (g) The information gained from step (b) and step (f) are then        used to provide the optimal signal information pertaining to the        signal feature or features under investigation.

More than one secondary band may be present. These additional secondarybands may be interrogated in the same way, i.e. steps (c) to (g).

In the context of breathing detection from the photoplethysmogram the‘primary band’ referred to in the above is the breathing band and the‘secondary band’ is the pulse band. In the method one or more or acombination of PPG signals may be employed.

In an alternative methodology once the RAP and RFP signals have beenabstracted in step (d) these are then interrogated over short segmentsusing an alternative time-frequency or frequency based method (e.g.using a standard FFT routine to find a dominant peak associated with theprimary band signal) or another method of signal repetition including,but not limited to, turning points of the signal. This may be employedto speed up the computation of the characteristic frequency of the RAPand RFP scalogram bands or to enhance the technique.

In step (d) above a combination of the RAP and RFP signals may also beused to generate a representative signal for secondary waveletdecomposition.

Patient respiration information from the secondary wavelet featuredecoupling incorporating the RAP and RFP signals is used directly tomonitor patient respiration. This can include the measurement ofbreathing rate and the identification of abnormal breathing patternsincluding the cessation of breathing. Either the RAP-based SWFD or theRFP-based SWFD information may be chosen for patient respirationmonitoring. Alternatively a combination of both may be employed wherethe respiration information derived from each method may be gradedquantitatively according to a confidence measure.

Further the respiration information gained from the RAP-based SWFD andthe RP-based SWFD may be compared to and/or combined with respirationinformation gained from other methods to provide an optimal output forrespiration measures including respiration rate, breath timings,breathing anomalies, etc. These other methods may include that describedin International Patent Application No PCT/GB02/02843, “Wavelet-basedAnalysis of Pulse Oximetry Signals” by Addison and Watson. The chosenrespiration measure for output will be extracted using a pollingmechanism based on a quantitative a measure of the quality of therespiration information derived by each method.

FIGS. 6 to 10 illustrate the preferred embodiment of the respirationmonitoring methodology. The wavelet transform of the PPG signal (FIG. 6(a)) is computed. A plot of the resulting scalogram is shown in FIG. 6(b). The 10 second PPG signal used in this example was taken from apremature neonate. The same methodology also works for adult and childPPGs. The pulse ridge is shown plotted as a black path across thescalogram in FIG. 6( b) at around 2.5 Hz—typical for these youngpatients. The RAP and RFP signals are then derived from the pulse ridgeof the wavelet transform. The RAP and RFP signals are shown respectivelyin FIG. 6( c) and FIG. 6( d). Also shown in FIG. 6( c) is the patientswitch signal which shows inspiration and expiration of the patient as ahigh/low amplitude square wave trace. The switch signal was activated byan observer monitoring the movement of the chest wall of the neonateduring the experiment. The turning points in the RAP and RFP signals maybe used as an initial detection, mechanism for individual breaths. TheRFP and RAP signals are assessed for quality using a confidence measure.This measure may be based on any reasonable measure including but notlimited to the entropy of the signals. The signal with the highestconfidence is used to extract information on individual breaths and abreathing rate using the average duration of a number of recentlydetected breaths. A second a wavelet transform is performed on bothsignals. The result of a second wavelet transform on the RAP signal ofFIG. 6( c) is shown in FIG. 7( a) and the ridges of this transformsurface are extracted as shown in FIG. 7( b). The result of a secondwavelet transform on the REP signal of FIG. 6( d) is shown in FIG. 7( c)and the ridges of this transform surface are extracted as shown in FIG.7( d).

The extracted ridges from the RFP and RAP signal transforms and theridges found in the original transform in the region of respiration,shown in FIGS. 8( a) (b) and (c) respectively, are then analysed todetermine a composite path which we call the ‘selected respiration path’SRP. The analysis may include, but is not limited to, the intensitiesand locations of the ridges. The SRP represents the most likelybreathing components. The SRP derived from the extracted ridges shown inFIGS. 8( a), (b) and (c) is shown in FIG. 8( d). The SRP will normallybe determined within an initial pre-determined “latch-on” time windowand reassessed within an updated time window. The ridge selectionprocedure used to derive the SRP is based upon a decision treeimplementing a weighted branching dependent upon, but not limited to,the following local (i.e. relationship between ridge components within aparticular ridge set) and global (i.e. the inter-relationship betweenridge components across ridge sets) criteria start and end position,length, average and peak strengths, various spatial (i.e. movement rangeover the time-frequency surface) statistical parameters includingvariance and entropy, and a measurement of relative switchback positions(i.e. degree of overlap with other ridges). These criteria are based onresults of our in house experimentation across a range of patientcategories: adult child and neonate.

A confidence metric for the accuracy of the SWFD ridge obtained from theRAP signal can also be acquired by comparing the resultant SWFD ridgeintensities derived from the RAP signal of the band maxima ridge andridges off-set from it. When compared to PAP-SWFD derived from the bandmaxima ridge, the off-ridge transform's ridges associated withrespiration have been observed to increase (to a maximum) in intensityas the displacement of the off-ridge from the maxima ridge is increased.Those ridges associated with other features, however, remain relativelystatic in amplitudes. In this way, by interrogating the ridge amplitudesof a plurality of RAP signals derived from the band maxima offsets, theridge or ridges associated with respiration can be identified through asignificant change in amplitude relative to others.

The selected ridge path (SRP) is then used to provide an overallconfidence as to breathing rate and/or provide individual breathmonitoring and/or prediction. By superimposing the SRP shown in FIG. 8(d) onto the phase information derived from the original transform thephase along the SRP can be a determined as shown in FIG. 9. In this wayindividual breaths may be identified through the behaviour of the phasecycling. This phase information along the SRP path may used to derive abreathing signal either by displaying the phase information as shown inFIG. 9 or by taking the cosine, or similar function, of the phaseinformation to produce a sinusoidal waveform, or by some other method toprovide a waveform of choice for visual display of the breathing signal.In an alternative embodiment the phase information from one of thesecondary transforms or a combination of the phase information from alltransforms may be used in the method. In addition, the phase informationused may be processed to remove erroneous phase information for examplecaused by movement artifact.

Parts of the SPR may contain missing segments caused by, for example,signal artefact. In these regions the SRP may be inferred using the lastavailable path point and the next available path point as shownschematically in FIG. 10. In the preferred embodiment this is carriedout using a linear fit between the points. However, other methods mayalso be used without departing from the scope of the invention.

3.3 Oxygen Saturation Measurement

The amplitude of signal features scale with their wavelet transformrepresentation. Thus by dividing components of the wave let transform ofthe red PPG signal by those of the infrared PPG signal we obtain newwavelet-based representations which contain useful information on thesignal ratios for use in the determination of oxygen saturation. If acomplex wavelet function is used this information may be extracted usinga suitable path defined on the ratio of the moduli of the transforms orusing a Lissajous plot from the real or imaginary parts of thetransforms. If a wavelet function containing only a real part isemployed then this information should be extracted using a Lissajousplot derived from the transforms. Two complimentary methods for theextraction of the wavelet-based ratio information required for thedetermination of oxygen saturation are given below.

FIG. 11 shows the three dimensional plots of the real-parts of thewavelet transforms of the simultaneously collected red and infrared PPGsignals. A complex Morlet wavelet was used in the transform. Thedominant nature of the pulse band and breathing band regions is evidentin the figure. These are marked ‘B’ and ‘C’ respectively in the figure.A secondary band containing pulse components can also be seen in thefigure (marked ‘A’). This band is associated with the double humpedmorphology of the PPG waveform. In the new wavelet-based Lissajousmethod a number of frequency levels are selected within a moving window.The moving window is shown schematically on the plot in FIG. 12. (Herewe use a 4.56 second window for the purpose of illustration althoughalternative window lengths may be used as required.) The oscillatorynature of the pulse band and breathing band regions is evident in theplot. The wavelet transform values along each of these frequency levelsfor the red and infrared signals are plotted against each other to givea Wavelet-Based Lissajous (WBL) plot. This results in a multitude of WBLplots, one for each frequency level selected. In the method, theselected frequency levels lie in the range of expected pulse frequencieswhich is, for the purposes of illustration, herein defined as between0.67 and 3.33 Hz. This range may be altered to reflect the application.The multitude of WBL plots may be displayed together to form a 3-DLissajous figure, as shown in FIG. 13( a).

Note that, in the example shown here, a complex wavelet function wasused and hence both real or both imaginary values of the transform canbe utilized in the method. Further, information from the real WBL plotsand imaginary WBL plots may be combined to provide an optimal solution.If a real-only wavelet function is used (i.e. a wavelet functioncontaining only a real part and no imaginary part) then only one set oftransforms (real) are available to use.

Each Lissajous plot making up the 3-D Lissajous figure is then probed tofind its spread both along its principle axis and that axis orthogonalto it. To do this, any reasonable measure of spread may be used. Here weemploy the standard deviation (SD). FIG. 13( b) shows an end on view ofthe 3-D Lissajous of FIG. 13( a). The region of the 3-D Lissajous FIGS.13( a) and 13(b) in the vicinity of the pulse frequency is marked by theletter ‘B’ in the figures and higher frequencies are marked by theletter ‘A’. FIG. 14 contains plots of the standard deviation of dataspread along the principle axis (top plot) and minor axis (middle plot)and the ratio of the standard deviations (lower plot) for each Lissajouscomponent making up the 3-D Lissajous plot in FIG. 13( a). In thepreferred embodiment the Lissajous component with the maximum spread isused in the determination of the oxygen saturation. The location of thiscomponent is marked by the arrow in the top plot of FIG. 14. Thiscomponent with the maximum spread along the major principle axis, isplotted in FIG. 13( c): the representative slope of which is computedand used to determine the local oxygen saturation value using apredefined look-up table. This maximum spread is usually found at ornear the pulse frequency. A check is also made on the SD ratios: definedas the SD of spread along the major axis divided by the SD of spreadalong the minor axis. A low SD ratio implies good correlation betweenthe two signals. The SD ratio for the component with maximum spread isindicated by the arrow in the lower plot of FIG. 14. We can see for thiscase that a relatively low SD ratio occurs at this location. The SDratio check may be used to pick a more appropriate wavelet-basedLissajous plot and can form part of a noise identification and/orreduction algorithm. Alternate methods of picking an optimalwavelet-based Lissajous may also be employed as appropriate. Duringperiods of excessive noise, the Lissajous components can become spreadout in shape, and in some cases the direction of the major and minorprinciple axis can significantly change from that of the relativelynoise free portions of the signals. A check can therefore be made todetermine if this has occurred by retaining a recent history of theselected Lissajous components. This can further be used as a confidencecheck on the selected Lissajous figure used in the determination ofoxygen saturation.

Note that the ratio of the amplitudes of the independent wavelet signalsmaking up the selected Lissajous component may also be used to determinethe oxygen saturation. Note also that the inverse transform of thesewavelet signals may be used in the method to determine oxygensaturation. The method described can be used to extract the pertinentratio information from wavelet transforms computed using either complexor real-only wavelet functions.

FIG. 15 shows the oxygen saturation determined using the 3-D Lissajousmethod (solid black line) compared with the traditional signal amplitudemethod (dotted) and signal Lissajous method (dashed). All three methodsemployed a 4 second smoothing window. It can be seen that for theparticular example signal interrogated here (the signals taken from thefinger of a healthy male patient aged 42 sitting in an upright positionat rest) the wavelet method produces a more consistent value.

FIG. 16 contains three-dimensional views of the red and infraredscalograms corresponding to an example PPG signal. Here the modulus ofthe complex transform is used. The locations of the band associated withthe pulse component are indicated in the plots (denoted ‘B’ in thefigures) We define the collection of points corresponding to the path ofthe maxima of the band projected onto the time frequency plane as P. Awavelet ratio surface (R_(WT)) can be constructed by dividing thewavelet transform of the logarithm of red signal by the wavelettransform of the logarithm of the infrared signal to get atime-frequency distribution of the wavelet ratio surface, i.e.

$\begin{matrix}{R_{WT} = \frac{{T\left( {a,b} \right)}_{R}}{{T\left( {a,b} \right)}_{IR}}} & \lbrack 7\rbrack\end{matrix}$

where the subscripts R and IR identify the red and infrared signalsrespectively. The wavelet ratio surface derived from the two scalogramsin FIG. 16 is shown schematically in FIG. 17. Note that as describedpreviously in our definition of scalogram we include all reasonableforms of rescaling including the original unsealed waveletrepresentation, linear rescaling and any power of the modulus of thewavelet transform may be used in the definition. As the amplitude of thewavelet components scale with the amplitude of the signal componentsthen for regions of the surface not affected by erroneous signalcomponents the wavelet ratio surface will contain values which can beused to determine the oxygen saturation using a pre-defined look-uptable.

As can be seen in FIG. 17, the time frequency wavelet ratio surfacealong, and in close proximity to, the projection of the pulse ridge pathP onto the wavelet ratio surface are stable and hence may be used in therobust determination of the oxygen saturation. In the preferredembodiment the values obtained along the projection of P onto R_(WT) areused to determine oxygen saturation via a pre-defined look-up tablewhich correlates R_(WT) to oxygen saturation.

A 2-D or 3-D view of the R_(WT) plot may be computed and displayed inreal time to provide a visual indication of the quality of the ratio ofratios obtained by the method, and hence the quality of the measurementof oxygen saturation.

FIG. 18 contains a plot of the end view of the wavelet ratio su faceshown in FIG. 17. From the figure we see that a relatively stable, flatregion is also found at or near the respiration frequency (R in thefigure). It has been noted from experimentation that for some cases therespiration region of the wavelet ratio surface may lie at a differentlevel from the pulse band region. Hence, for these cases, using R_(WT)obtained in the breathing region % would produce erroneous values ofoxygen saturation. By following a path in the region of the pulse bandour method automatically filters out erroneous breathing components inthe signal.

FIG. 19 contains a plot of the oxygen saturation determined by thewavelet ratio surface method as a function of time as compared with twostandard methods: the traditional signal amplitude method and thetraditional Lissajous method. The PPG signals were again taken from thefinger of a healthy male patient aged 42 sitting in an upright positionat rest. From visual inspection of the plot it can be seen that, forthis example, the wavelet-based method produces a more consistent valueof oxygen saturation compared to contemporary methods.

It will be recognized by those skilled in the art that, in analternative embodiment, the pulse band ridge path P can also beprojected onto the real or imaginary transform components. From thevalues of the transform components along this path over a selected timeinterval a Lissajous figure may be obtained and used in thedetermination of oxygen saturation. It will also be recognized by thoseskilled in the art that, in an alternative embodiment, alternative pathsmay be projected onto the wavelet ratio surface and used for thedetermination of oxygen saturation. For example in regions where thepulse band exhibits noise causing the path of the ridge maxima to movefar from the actual pulse frequency a method for detecting such noisyevents and holding the path to the most appropriate recent pulsefrequency may be used until the event has passed or until a presetperiod of time whereby an alarm is triggered.

The 3-D Lissajous and wavelet ratio surface methodologies for thedetermination of oxygen saturation, as described above, can form thebasis of an algorithm for incorporation within pulse oximeter devices.Furthermore the ability of the methodologies to restrict themselves tothe optimal wavelet transform values by picking the optimal Lissajous orfollowing the pulse band respectively, allows for erroneous signalelements to be discarded automatically; so leading to a more robustalgorithm for the determination of oxygen saturation.

Note that in both new methods the inverse transform of the selectedwavelet values may also be used as they too scale with the signalfeatures.

In the preferred embodiment, both the 3-D Lissajous and wavelet ratiosurface methods are employed simultaneously and the optimal measuredsaturation value determined. It is obvious from the above descriptionthat the initial inputted signals and wavelet transformation of thesesignals form common elements to both methods.

Those skilled in the art will recognise that modifications andimprovements can be incorporated to the methodology outlined hereinwithout departing from the scope of the invention.

Those skilled in the art will recognise that the above methods may beperformed using alternative time-frequency representations of thesignals where the amplitude in the time-frequency transform space can berelated to the amplitude of pertinent features within the signal.However, in the preferred method the continuous wavelet transform isemployed.

In summary a method for the decomposition of pulse oximetry signalsusing wavelet transforms has been described which allows for underlyingcharacteristics which are of clinical use to be measured and displayed.These wavelet decompositions can then be used to:

-   -   (a) provide, using information derived from the signal wavelet        transforms (i.e. from the original transform, the rescaled        wavelet transforms, the ratio of derived wavelet transforms, the        scalograms, wavelet ridges, etc.) a method for measuring oxygen        saturation.    -   (b) construct, using information derived from the wavelet        transform (i.e. from the original transform, the rescaled        wavelet transforms, the ratio of derived wavelet transforms, the        a scalograms, wavelet ridges, etc.), a plurality of        wavelet-based Lissajous figures from which the optimum Lissajous        representation is chosen using preset criteria and the slope of        which is used to determine the oxygen saturation of the signal        using a look-up table.    -   (c) construct, using information derived from the wavelet        transform (i.e. from the original transform, the rescaled        wavelet transforms, the ratio of derived wavelet transforms, the        scalograms, wavelet ridges, etc.), a time-frequency equivalent        of the ratio of ratios, the wavelet ratio surface, from which to        determine the oxygen saturation of the signal by following a        selected path through the time frequency plane. The preferred        path through the time frequency plane to be that corresponding        to the pulse band.    -   (d) provide an optimal oxygen saturation value from those        derived in (b) and (c).

3.4 The Monitoring of Patient Movement

Current devices are configured to remove detrimental movement artifactfrom the signal in order to clean it prior to determination of theclinical parameter of interest, e.g. the pulse rate or oxygensaturation. However, the method described herein as embodied within adevice monitors general patient movement, including large scale bodymovements, respiration and the beating heart. In this way the absence ofpatient movement and/or irregularity of movement can be detected and analarm triggered.

Patient movement results in PPG signal artifact. The manifestation ofthis artifact can be observed in the wavelet transform of the signal. Anexample of a movement artifact in the scalogram is shown in FIG. 20( a).The PPG signal from which the wavelet plot was derived was acquired froma premature baby a few weeks after birth. The location of the movementartifact is marked by the arrow in the plot. The breathing band ridgehas been superimposed on the wavelet plot (marked R in the figure). Thepulse band is marked P in the figure. Notice that the artifact causes adrop-out in the detected breathing ridge (i.e. a missing fragment), andalso cuts through the pulse band where it can cause similar drop outs tooccur in the detection of the pulse ridge. It has been the focus ofpulse oximeter device manufacturers to remove as much of the movementartifact component from the signal while leaving the informationnecessary to obtain accurate oxygen saturation and pulse ratemeasurements. In a preferred embodiment of the methods described hereinwe extract a movement component from the PPG signals for use in themonitoring of patient movement and, in particular, for the monitoring ofthe movement of infants.

A three-dimensional view of the scalogram of FIG. 20( a) is plotted inFIG. 20( b). Here we see the dominance of the movement artifact featurein wavelet space. By identifying such features we can monitor patientmovement. It is common for young babies to exhibit very variablerespiration patterns and to cease breathing for short periods of time,especially when making a movement of the body. Hence inspecting thederived movement signal when an irregular respiration signal occurs,including cessation of breathing, gives a further measure of patientstatus.

The modulus maxima of the wavelet surface is the loci of the maxima ofthe wavelet surface with respect to time. FIG. 21( a) plots the modulusmaxima lines associated with FIG. 20( a). FIG. 21( b) shows athree-dimensional view of the transform surface with the modulus maximalines superimposed. FIG. 22( a) shows an end view of the maxima lines(without the surface shown) corresponding to those shown in FIGS. 21( a)and 21(b). We can see from the end view that the modulus maxima linecorresponding to the movement artifact has a significantly differentmorphology to the other maxima lines it covers a large frequency rangeand contains significantly more energy than the other maximal especiallyat low frequencies. By setting amplitude threshold criteria at afrequency or range of frequencies we can differentiate the modulusmaxima of the artifact from other features.

An example of this is shown schematically by the threshold level andfrequency range depicted on FIG. 22( b), where maxima above thepre-defined amplitude threshold within a frequency range given byf₍₁₎<f<f₍₂₎ are identified as corresponding to movement artifact. Inaddition a check of local anomalies in the detected pulse and breathingridges may also be made. For example modulus maxima which are atsignificantly higher amplitudes than the pulse ridge mean value in theirvicinity are deemed to correspond to movement artifact. This is depictedin FIG. 22( c). In addition, modulus maxima which are at a significantlyhigher amplitude than the respiration ridge mean value in their vicinityare deemed to correspond to movement artifact. This is depicted in FIG.22( d).

A region in the time frequency plane within the support of the waveletis then deemed to contain artifact. The support of the wavelet is takenas a predefined measure of temporal ‘width’ of the wavelet. For waveletswith theoretical infinite width, such as the Morlet wavelet, the widthis defined in terms of the standard deviation of temporal spread: forexample we use three times the standard deviation of spread each sidefrom the wavelet centre. Thus a cone of influence of the artifact may bedefined in the transform plane.

Using the above method we can monitor patient movement by detectingmodulus maxima corresponding to movement artifact. This information canbe used to monitor patient movement and/or to provide a measure ofconfidence on the derived values of other measurements (e.g. oxygensaturation, pulse and respiration). These measurements may, for examplebe held at a previous value until the detected movement event haspassed.

Other artefact may exist in the signal which may originate from thedrive and control electronics including, but not limited to, automaticgain adjustments. The occurrence of this type of artifact will be knownand can be accounted for in the signal and hence differentiated frommovement artifact.

4. DEVICE CONFIGURATION AND USAGE

The device may be used to monitor one or more of the following signals:respiration, pulse, breathing and movement. Useful information regardingthese signals would be displayed on the device or output in a suitableformat for use.

In one embodiment the device would be used to continually monitor one ormore of these signals.

In another embodiment the device would be used to monitor one or more ofthese signals intermittently.

4.1 Device Configuration

Detailed block diagrams of the device are provided in FIGS. 23, 24, 25and 26.

The following is with reference to FIG. 23. In the present inventionsignals are acquired at the patient's body 10. These are sent fordigitization 11. The links between components of the system may be fixedphysical or wireless links, for example radiofrequency links. Inparticular, either or both of the links between 10 and 11 or 11 and 12,or the links between the analyser component and a visual display may awireless link enabled by a radiofrequency transmitter. The digitisedcardiac signals 11 are sent to 12 where in the preferred embodiment thenatural logarithm of the signals are computed. These are then sent to 13where the wavelet transforms of the signals are performed. Thecomponents of the wavelet transformed signals, including modulus, phase,real part, imaginary part are then sent to 14 where the pulse ridge isidentified. The information from 13 and 14 is then used in theextraction of patient pulse information 15, oxygen saturation 16,patient movement information 17 and respiration information 18. Theinformation regarding oxygen saturation, pulse, respiration and patientmovement is all sent to the Analyser component 19 where it is collectedand collated ready for outputting at 20. The oxygen saturation,respiration pulse rate and movement information is output from thedevice 20 through a number of methods, which may include a printout, adisplay screen or other visual device, an audable tone, andelectronically via a fixed or remote link. The output information may besent to a location remote from the patient, for example sent viatelephone lines, satellite communication methods, or other methods.Further, real-time wavelet-based visualisations of the signal (includingthe original transform and/or the wavelet ratio surface with projectedpulse ridge path) may be displayed on the device 20. Thesevisualisations will highlight salient information concerning the qualityof the outputted measurements. Additional useful information regardingmovement artefact and breathing information may be apparent from such areal time display.

The workings of components 15, 16, 17 and 18 shown in FIG. 23 aredescribed below in more detail.

Pulse Component 15:

With reference to FIG. 23, pulse information including pulse rate andpulse irregularities are derived at 15 using the instantaneous frequencyof the pulse band ridge determined at 14. The instantaneous frequencymay correspond directly with the instantaneous ridge frequency orrequire a mapping from % the instantaneous ridge frequency and the truerespiration rate. Further the method allows for a smoothing of thisvalue over a fixed time interval. Further the method allows forerroneous values of the pulse rate derived in this way to be excludedfrom the outputted values. This component 15 may also be used to measureinter-beat intervals and pertinent pulse wave timings. The pulseinformation determined at 15 is then sent to the Analyser Component 19.

The Oxygen Saturation Component:

The following is with reference to FIGS. 23 and 24. The oxygensaturation component 16 shown in FIG. 23 comprises the subcomponents 31,32, 33, 34, 35, 36 and 37 as shown in FIG. 24. The wavelet transforminformation and pulse ridge information from 14 is input into thismodule at the feature sorter 31 which sends the relevant information tothe Lissajous computation unit (components 32, 33 and, 34) and the pulseridge computational unit (components 35 and 36), A predetermined numberof wavelet-based Lissajous are computed over the pulse region 32. Anautomated procedure is employed for the determination of the optimalLissajous for use in the oxygen saturation calculation 33. In thepreferred embodiment this would be achieved by comparing the standarddeviations of the data spread along of the principle axes of theLissajous plot. The slope of the principle axis is then used todetermine the oxygen saturation using a suitable look-up table whichcorrelates the slope to oxygen a saturation 34. The oxygen saturationdetermined at 34 is denoted ‘Oxygen Saturation Determination (1)’.

The information regarding the wavelet transforms of the PPG signals andthe path of the pulse ridge is collected at the feature sorter 31 usedto compute the wavelet ratio surface 35. The wavelet ratio correspondingto the pulse path is determined by projecting the pulse path onto thewavelet ratio surface. This ratio is then used to determine the oxygensaturation using a look-up table which correlates the wavelet ratio tooxygen saturation 36. The oxygen saturation determined at 36 is denoted‘Oxygen Saturation Determination (2)’. The two oxygen saturation values(1) and (2) are then used to determine the most appropriate value ofoxygen saturation 37. This value is then sent to the Analyzer Component19.

Movement Component 17:

The following is with reference to FIGS. 23 and 26. The Movementcomponent 17 of FIG. 23 comprises the subcomponents 51, 52, 53, 54, 55as shown in FIG. 26. The wavelet transform information and pulse ridgeinformation is sent from 14 to the modulus maxima component 51 where themodulus maxima of the wavelet surfaces are computed. The modulus maximainformation is then sent, to be analysed for movement artifact. Themodulus maxima information is sent to the components 52, 53 and 54.These are described as follows. The Threshold component 52 detectsmaxima above a preset threshold and within a preset frequency rangewhich are them defined as movement artifact. The Pulse Check component53 checks the maxima corresponding to the pulse band to see ifanomalously large excursion from the local mean level has occurred. Ifso movement artifact is detected. The Respiration Check component 54checks the maxima in the vicinity of the selected respiration path SRPobtained from 18 to determine if anomalously large excursion from thelocal mean level has occurred. If so movement artifact is detected. Theinformation from components 52, 53 and 54 are then collected andcollated at the Movement Signal component 55 where a movement signal isgenerated. This is then, sent to the Analyser Component 19.

Respiration Component 16:

The following is with reference to FIGS. 23 and 25. The respirationcomponent 17 of FIG. 23 comprises the subcomponents 61, 62, 63 and 64 asshown in FIG. 25. The wavelet transform and pulse ridge information from14 are input into this module at component 61 which uses the informationto derive the ridge amplitude perturbation (RAP) signal and the ridgefrequency perturbation (RFP) signals. The RAP and RFP signals arederived using the path defined by the projection of the maxima of thepulse band or a locus of points displaced from this maxima path. Asecondary wavelet transform is performed on these signals 62 and thenpassed to the respiration detection component 63 where the respirationridges are detected for the wavelet transforms of the RFP and RAPsignals. These are then used within an algorithm which decides theselected respiration path (SRP). This algorithm may also incorporaterespiration information using complementary methods 64. Note that in themethod the original transform obtained at 13 and the secondary transform62 may be computed using different wavelet functions. The respirationinformation is then sent to the Analyzer Component 19 and also to theMovement component 17.

The Analyser Component 19:

With reference to FIG. 23, the Analyzer Component collects theinformation from the pulse component 15, Oxygen Saturation Component 16,Movement Component 17 and Respiration Component 18. During periods ofdetected motion or other signal artifact the analyzer makes a decisionto hold the most appropriate recent values of these signals until theartifact event passes or until predetermined interval has passed atwhich point an alarm signal sent to the device output 20. Further theanalyzer checks the incoming signals for anomalous behaviour including,but not limited to: low and or high pulse rates, pulse irregularities,low and high breathing rates, breathing irregularities, low and highoxygen saturation rates movement irregularities including excessivemovement and absence of movement. Detected anomalous behaviour orcombination of behaviours will trigger an alarm signal sent to thedevice output 20.

4.2 Physical Attachment of Probes and Transmission of PPG Signals

Referring to FIG. 23, the acquisition of the signal 10 takes place at asuitable site on the patient's body. This signal is then sent tocomponent 11 where the signals are digitized then to component 12 wheretheir natural logarithm is computed prior to the wavelet analysis at 13.The patient signal may be taken using a standard probe configuration.For example a finger or toe probe, foot probe forehead probe, ear probeand so on. Further the probe may function in either transmittance orreflectance mode.

In one preferred embodiment for use with neonates a foot/ankle mounteddevice such as a cuff is employed as depicted schematically in FIG. 27.The cuff is used to house the probe electronics, radio frequencytransmitter modules and battery. FIG. 27( a) shows the patients lowerleg 80 and foot with the preferred embodiment of the cuff 83 attached tothe foot. The patients heel 81 and toes 82 protrude from the cuff. FIG.27( b) shows two views, one from each side of the foot showing the cuffwith compartments for housing the electronic equipment required forsignal acquisition and transmission. The PPG signals may be takendirectly through the foot using Light Emitting Diodes (LEDs) 86 andphotodetector 88 located as shown or, in an alternative embodiment, theymay be taken at the toe using a short length of cable attaching thepulse oximeter probe to the electronics contained in the cuff. In afurther alternative embodiment reflectance mode photoplethysmography maybe employed. In a further alternative embodiment more suitable for adultmonitoring the electronic equipment is packaged within a soft housingwhich is wrapped and secured around the wrist as shown in FIG. 28. Theelectronic components for receiving processing and transmitting the PPGsare housed in a unit 90 secured by a band 91 to the patients wrist. ThePPG signals are acquired at a site local to the wrist band. For examplefrom a finger 93 via a lead 92 from the wrist unit 90, or at the site ofthe wrist band and housing, for example, reflectance modephotoplethysography. In yet another alternative embodiment, the signalfrom the pulse oximeter probe would be sent to the monitor device usinga physical lead instead of the wireless method described here.

Light transmitters other than LEDs may be used in the device withoutdeparting from the scope of the invention.

In an alternative embodiment, the digitised signal from 11 may inputdirectly to the wavelet transform component 13 without, taking thenatural logarithm.

In an alternative embodiment, more than two wavelengths or combinationof more than two wavelengths of light may be employed in the Oximetrymethod.

4.3 Use of the Device 4.3.1 General Use

The device may be used for general patient monitoring in the hospital,home, ambulatory or other environment. For example in a preferredembodiment for a device for use within a hospital setting it may be usedto continually or intermittently monitor patient respiration togetherwith oxygen saturation and pulse rate.

4.3.2 Embodiment as an Apnea Monitor

In another preferred embodiment of the device it would be used as anapnea monitor. Apnea is the cessation of breathing usually occurringduring sleep. There is increasing awareness of this sleep disorder asthe cause of a number of serious medical conditions in adults andinfants. Separate areas of use are envisaged for the device as an apneamonitor. Examples of this use include, but are not limited to: (1) adultmonitoring, where it can be used as a home screening diagnostic tool forpotential apnea patients and (2) infant monitoring, where it can be usedas either an in hospital or home monitoring tool to alert the child'scarer to this potentially fatal respiration irregularity.

Apnea monitors monitor heart and respiratory signals to detect apneaepisodes—usually defined as cessation of breathing for >20 seconds.Apnea is a associated with slowing of the pulse (bradycardia) or bluishdiscoloration of the skin due to lack of oxygenated haemoglobin(cyanosis). Long term effects of apnea in adults are quite serious andhave been reported to include: heavy snoring, weariness and obsessivedrive to fall asleep, reduced physical and mental fitness, strokes,nervousness, fall in concentration and headaches, psychic symptom, up todepressions, sexual dysfunctions, impotence, dizziness and nightlyperspiration. In babies apnea may lead to death if suitableresuscitation measures are not taken.

As it measures respiration and movement directly from the pulse oximetersignal (in addition to oxygen saturation and pulse), the device can befitted remote from the head; e.g. the foot or arm of the patient. Thishas the advantage over current devices which comprise of probes locatedon the patients head mad face to measure breathing at the patients noseand/or mouth. As such they are uncomfortable for adult patients and arequite impractical for fitting to babies for the obvious reason ofcausing a potential choking hazard. The preferred embodiment of ourinvention allows the PPG signal collected at the patient to be sent viaa wireless link to a remotely located device.

In summary, embodied as an apnea monitor, the device provides a methodfor the acquisition analysis and interpretation of pulse oximetersignals to provide clinically useful information on patient pulse rate,oxygen saturation, respiration and movement. From a combination of someor all of this information clinical decisions can be made with regard tothe patient's health. The patient respiration information is used tomonitor the patient in order to compute a respiration rate and to detectbreathing abnormalities, for example: apnea events, cessation inbreathing, sharp intakes of breaths, coughing, excessively fastbreathing, excessively slow breathing, etc. Information derived from oneor more of the respiration, movement, oxygen saturation and pulsemeasurements may be used to trigger an alarm to call for medical help orto initiate an automated process for the administration of a therapeuticintervention. A method may be employed for the archiving of the derivedsignals during the analysis period of the patient which may be used at alater date for analysis by the clinician.

The device may be used to monitor the patient both during sleep and whenawake.

The device may be used to detect the onset of sudden infant deathsyndrome SIDS by detecting and analysing abnormalities in themeasurement of one or more of the following oxygen saturation,respiration, movement and pulse.

4.3.3 Alarm

As described above, it is envisaged that the gathered information isused to trigger an alarm at the bedside and/or at a remote nursingstation. This alarm would be graded according to a classification ofpatient information. For example a reduction in oxygen saturation belowa predefined threshold with associated loss or irregularity of patientmovement, irregularity of pulse rate and loss or irregularity of patientrespiration could trigger the highest level of alarm, whereas areduction of oxygen saturation below a predefined threshold with anormal level of patient movement and/or a regular respiration pattern,could trigger a lower level of alarm.

5. BRIEF DESCRIPTION OF DRAWINGS

FIG. 1( a): A wavelet transform surface showing the pulse band (locatedbetween the dashed lines). (High to Low energy is graded from white toblack in the grey scale plot.)

FIG. 1( b): Three-dimensional view of the wavelet transform surface ofFIG. 1( a) showing the maxima of the pulse band with respect tofrequency (the ridge) superimposed as a black path across the bandmaxima. (High to Low energy is graded from white to black in the greyscale plot.)

FIG. 2: 3-D Schematic of a wavelet transform surface containing twobands. The locus of the local maxima on the bands (the ‘ridges’) areshown by dashed lines.

FIG. 3: Schematics of the RAP (top left) and RFP (top right) signalsderived from ridge A in FIG. 1 together with their corresponding wavelettransforms shown below each (in 2D).

FIG. 4( a): The SWFD method as applied to a pulse oximetersignal—Scalogram of Original Signal. (High to Low energy, is graded fromwhite to black in the grey scale plot.)

FIG. 4 (b): The SWFD method as applied to a pulse oximeter signal—3-Dview of scalogram in (a) with the path of the pulse band ridgesuperimposed. (High to Low energy is graded from white to black in thegrey scale plot.)

FIG. 4 (c) The SWFD method as applied to a pulse oximeter signal—RAPsignal (Top: full signal. Lower: blow up of selected region)

FIG. 4 (d): The SWFD method as applied to a pulse oximeter signal—RFPsignal (Top: full signal. Lower blow up of selected region)

FIG. 5( a): The SWFD method as applied to a pulse oximeter signal—RAPscalogram. (High to Low energy is graded from white to black in the greyscale plot.)

FIG. 5( b): The SWFD method as applied to a pulse oximeter signal—RFPscalogram. (High to Low energy is graded from white to black in the greyscale plot.)

FIG. 5( c): The SWFD method as applied to a pulse oximeter signal—3-Dview of RAP scalogram with breathing band ridge shown. (High to Lowenergy is graded from white to black in the grey scale plot.)

FIG. 5( d): The SWFD method as applied to a pulse oximeter signal—3-Dview of REP scalogram with ridge shown. (High to Low energy is gradedfrom white to black in the grey scale plot.)

FIG. 6( a): PPG Signal

FIG. 6( b): Pulse band and ridge corresponding to signal (a). (High toLow energy is graded from white to black in the grey scale plot.)

FIG. 6( c): RAP signal derived from ridge in (b) with breathing switch(square waveform) superimposed.

FIG. 6( d): RFP signal derived from ridge in (b)

FIG. 7( a): Wavelet Transform of RAP signal. (High to Low energy isgraded from white to black in the grey scale plot.)

FIG. 7( b): Extracted ridges from wavelet transform in (a). (High to Lowenergy is graded from white to black in the grey scale plot.)

FIG. 7( c): Wavelet Transform of RFP signal. (High to Low energy isgraded from white to black in the grey scale plot.)

FIG. 7( d): Extracted ridges from wavelet transform in (c). (High to Lowenergy is graded from white to black in the grey scale plot.)

FIG. 8( a): Breathing ridges extracted from the original wavelettransform

FIG. 8( b): Breathing ridges extracted from the secondary wavelettransform of the RAP signal

FIG. 8( c): Breathing ridges extracted from the secondary wavelet,transform of the RFP signal

FIG. 8( d): Selected respiration path (SRP).

FIG. 9: Transform Phase along the SRP

FIG. 10: Filling in missing segments of the SRP

FIG. 11: Wavelet Representations of the Red PPG (top) and Infrared PPG(bottom)

FIG. 12: Schematic of the Sliding Window used to Obtain the WaveletComponents for the 3-D Lissajous

FIG. 13( a): Wavelet-based 3-D Lissajous 3-D View.

FIG. 13( b): Wavelet-based 3-D Lissajous: End on View of (a).

FIG. 13( ): Wavelet-based 3-D Lissajous: End on View of SelectedComponent.

FIG. 14: Standard Deviation of Lissajous Components in FIG. 3, Top plot:SD of principle component; Middle plot SD of minor component; Lowerplot: Ratio of SD components. All three plots plotted against frequencyin Hz.

FIG. 15: Computed Oxygen Saturation Curves. Dotted line: SignalAmplitude Method; Dashed Line traditional Signal Lissajous Method; SolidLine: Wavelet-based 3-D Lissajous Method.

FIG. 16: The red and infrared wavelet modulus surfaces corresponding toa 45 second segment of PPG signals. (High to Low energy is graded fromwhite to black in the grey scale plot.)

FIG. 17: The wavelet ratio surface derived from the division of the redby the infrared wavelet 1, representations shown in FIG. 16.

FIG. 18: An end view of the wavelet ratio surface shown in FIG. 17.

FIG. 19: Computed Oxygen Saturation curves. Dotted line: OxygenSaturation from Traditional Signal Amplitude Method; Dashed Line; OxygenSaturation from Traditional Signal Lissajous Method; Solid Line; OxygenSaturation from Traditional Wavelet-Ratio Surface Method

FIG. 20( a): Wavelet transform plot of a PPG signal taken from a youngbaby showing a corresponding to patient movement. Low to high energy isdepicted from black to white in the greyscale plot.

FIG. 20( b). Three-dimensional view of (a) Low to high energy isdepicted from black to white in the greyscale plot.

FIG. 21( a): Transform plot of FIG. 20( a) with modulus maximasuperimposed. Low to high energy is depicted from black to white in thegreyscale plot.

FIG. 21( b): Three-dimensional view of FIG. 21( a). Low to high energyis depicted from black to white in the greyscale plot.

FIG. 22( a): End view of modulus maxima lines in FIG. 21( b).

FIG. 22( b): Amplitude threshold method of identifying modulus maximaassociated with movement artefact

FIG. 22( c): Pulse ridge-based method of identifying modulus maximaassociated with movement artefact

FIG. 2 d): Respiration ridge-based method of identifying modulus maximaassociated with movement artefact

FIG. 23: Block diagram of device configuration

FIG. 24: Block diagram of subcomponents of oxygen saturation component(16) shown in FIG. 23

FIG. 25: Block diagram of subcomponents of respiration component (18)shown in FIG. 23

FIG. 26: Block diagram of subcomponents of movement component (17) shownin FIG. 23

FIG. 27( a): Schematic of foot cuff mounting: soft housing surroundingfoot used to hold monitoring apparatus.

-   80 patient leg; 81 patient heel; 82 patient toes; 83 soft housing    surrounding foot

FIG. 27( b): View from both sides of the envisaged device: preferredembodiment for neonatal monitor. 84 connection cabling; 85 RF componentsattached to housing; 86 LEDs; 87 pulse oximeter components attached tohousing; 88 photodetector. (Note LEDs and photodetector may also belocated on toe using short cable length from cuff.)

FIG. 28: Schematic of wrist cuff mounting: 90 electronic componenthousing; 91 wrist band; 92 connector cable; 93 finger probe

6. GENERAL

The invention has been described and shown with specific reference tospecific embodiments. However it will be understood by those skilled inthe art that changes to the form and details of the disclosedembodiments may be made without departing from the spirit and scope ofthe invention. For example signal transforms other than the wavelettransform may be used. Other variations may include using a multiplexedarrangement which alternates measurements for pulse, oxygen saturation,respiration and movement artefact using variations of the acquisitionequipment and transmission electronics. These variations may include butare not limited to the use of more than two wavelengths of light andvariable power and/or variable duty cycle to the light transmitters.

7. REFERENCE

-   Addison P. S., ‘The Illustrated Wavelet Transform Handbook’,    Institute of Physics Publishing, 2002, Bristol, UK.

1-100. (canceled)
 101. A method for determining physiologicalinformation, comprising: obtaining a biosignal from a subject at leastin part using a signal acquisition unit; decomposing the biosignal atleast in part using a wavelet transform analysis to generate transformdata; identifying a path in a band based at least in part on thetransform data; decomposing the path into the frequency domain; anddetermining a dominant peak of the frequency domain to determinephysiological information of the subject.
 102. The method of claim 101,wherein the path comprises a ridge of the band or a path in a vicinityof a ridge of the band.
 103. The method of claim 102, wherein the pathcomprises time-scale points.
 104. The method of claim 102, wherein thepath comprises time-amplitude points.
 105. The method of claim 101,wherein the signal acquisition unit comprises a photodetector andwherein the biosignal is a photoplethysmogram signal.
 106. The method ofclaim 101, wherein decomposing the biosignal at least in part using thewavelet transform analysis comprises performing a continuous wavelettransform of the biosignal.
 107. The method of claim 101, wherein theband is a pulse band.
 108. The method of claim 101, further comprisingdetermining respiration rate information of the subject based at leastin part on the dominant peak of the frequency domain.
 109. The method ofclaim 101, further comprising identifying individual breaths of thesubject based at least in part on the dominant peak of the frequencydomain.
 110. The method of claim 101, further comprising identifyingbreathing irregularities of the subject based at least in part on thedominant peak of the frequency domain.
 111. A monitoring system fordetermining physiological information, the system comprising: a signalacquisition unit attachable to a subject to obtain a biosignal; and asignal processor configured to: decompose the biosignal at least in partusing a wavelet transform analysis to generate transform data; identifya path in a band based at least in part on the transform data; decomposethe path into the frequency domain; and determine a dominant peak of thefrequency domain to determine physiological information of the subject.112. The system of claim 111, wherein the path comprises a ridge of theband or a path in a vicinity of the band.
 113. The system of claim 112,wherein the path comprises time-scale points.
 114. The system of claim112, wherein the path comprises time-amplitude points.
 115. The systemof claim 111, wherein the signal acquisition unit comprises aphotodetector and wherein the biosignal is a photoplethysmogram signal.116. The system of claim 111, wherein the signal processor is configuredto decompose the biosignal at least in part using the wavelet transformanalysis by performing a continuous wavelet transform of the biosignal.117. The system of claim 111, wherein the band is a pulse band.
 118. Thesystem of claim 111, wherein the signal processor is further configuredto determine respiration rate information of the subject based at leastin part on the dominant peak of the frequency domain.
 119. The system ofclaim 111, wherein the signal processor is further configured toidentify individual breaths of the subject based at least in part on thedominant peak of the frequency domain.
 120. The system of claim 111,wherein the signal processor is further configured to identify breathingirregularities of the subject based at least in part on the dominantpeak of the frequency domain.